






'''simple docstring'''







from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         return 0.0





def       __UpperCamelCase					(      lowercase_       :  np.ndarray   ,       lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			min([-20, np.min(fft_results[1 : samplerate // 2 - 1]   )]   )
       a_						     =			max([20, np.max(fft_results[1 : samplerate // 2 - 1]   )]   )
       return lowest, highest


def       __UpperCamelCase					(      lowercase_       :  FilterType   ,       lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			512
       a_						     =			[1] + [0] * (size - 1)
       a_						     =			[filter_type.process(lowercase_   ) for item in inputs]

       a_						     =			[0] * (samplerate - size)  # zero-padding
       outputs += filler
       a_						     =			np.abs(np.fft.fft(lowercase_   )   )
       a_						     =			20 * np.logaa(lowercase_   )

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24   ,       samplerate / 2 - 1   )
       plt.xlabel('Frequency (Hz)'   )
       plt.xscale('log'   )

       # Display within reasonable bounds
       a_						     =			get_bounds(lowercase_   ,       lowercase_   )
       plt.ylim(max([-80, bounds[0]]   )   ,       min([80, bounds[1]]   )   )
       plt.ylabel('Gain (dB)'   )

       plt.plot(lowercase_   )
       plt.show()


def       __UpperCamelCase					(      lowercase_       :  FilterType   ,       lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			512
       a_						     =			[1] + [0] * (size - 1)
       a_						     =			[filter_type.process(lowercase_   ) for item in inputs]

       a_						     =			[0] * (samplerate - size)  # zero-padding
       outputs += filler
       a_						     =			np.angle(np.fft.fft(lowercase_   )   )

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24   ,       samplerate / 2 - 1   )
       plt.xlabel('Frequency (Hz)'   )
       plt.xscale('log'   )

       plt.ylim(-2 * pi   ,       2 * pi   )
       plt.ylabel('Phase shift (Radians)'   )
       plt.plot(np.unwrap(lowercase_   ,       -2 * pi   )   )
       plt.show()












'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : List[Any]      			=     ['''image_processor''', '''tokenizer''']
  _a  : List[Any]      			=     '''ViTImageProcessor'''
  _a  : int      			=     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')







  def __init__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                    ' instead.'   ,	UpperCamelCase__   ,	)
                a_						     =			kwargs.pop('feature_extractor'	)

         a_						     =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError('You need to specify an `image_processor`.'	)
         if tokenizer is None:
                raise ValueError('You need to specify a `tokenizer`.'	)

         super().__init__(UpperCamelCase__   ,	UpperCamelCase__	)







  def __call__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         if text is None and visual_prompt is None and images is None:
                raise ValueError('You have to specify either text, visual prompt or images.'	)

         if text is not None and visual_prompt is not None:
                raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'	)

         if text is not None:
                a_						     =			self.tokenizer(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if images is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None and images is not None:
                a_						     =			{
                    'pixel_values': image_features.pixel_values,
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         elif text is not None and images is not None:
                a_						     =			image_features.pixel_values
                return encoding
         elif text is not None:
                return encoding
         elif visual_prompt is not None:
                a_						     =			{
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         else:
                return BatchEncoding(data=dict(**UpperCamelCase__	)   ,	tensor_type=UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.batch_decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor_class







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor




